This report is based on a model described in a paper presented at the 2021 International Conference on Evolving Cities, University of Southampton, 22 – 24 September 2021.
If you want to cite the method/model please use:
Anderson, B. (2021). Simulating the consequences of an emissions levy at the city and neighbourhood scale. Paper presented at the International Conference on Evolving Cities, MAST Mayflower Studios, Southampton, United Kingdom. 22 - 24 Sep 2021.
If you wish to re-use material from this report please cite it as:
Ben Anderson (2021) Simulating a local emissions levy to fund local energy effiency retrofit : All English LSOAs. University of Southampton, United Kingdom
License: CC-BY
Share, adapt, give attribution.
This report estimates the value of an emissions levy using LSOA level data on emissions derived from the CREDS place-based emissions calculator. These emissions are all consumption, gas and electricity. It does this under two scenarios - a simple carbon value multiplier and a rising block tariff.
It then compares these with estimates of the cost of retrofitting EPC band dwellings D-E and F-G in each LSOA and for the whole area under study.
Key results:
Background blurb about emissions, retofit, carbon tax/levy etc
In the reminder of this paper we develop a model of an emissions levy using LSOA level data on emissions derived from the CREDS place-based emissions calculator.
We apply carbon ‘values’ to a number of emissions categories to estimate the levy revenue that would be generated for each LSOA in year 1 of such a levy. We then sum these values to given an overall levy revenue estimate for the area in the case study.
We then use estimates of the cost of retrofitting EPC band dwellings D-E and F-G together with estimates of the number of such dwellings in each of the LSOAs to calculate the likely cost of such upgrades in each LSOA and for the whole area in the case study.
We then compare the distributions of the two to understand whether sufficient revenue would be generated within each LSOA to enable the per-LSOA or whole case study area costs of the energy efficiency upgrades to be met. In doing so we also analyse the extent to which redistribution of revenue from high emissions areas (households) would be required.
It should be noted that this is area level analysis using mean emissions per household. It will not capture within-LSOA heterogeneity in emissions and so will almost certainly underestimate the range of the household level emissions levy value.
NB: no maps in the interests of speed
We will use a number of Lower Layer Super Output Area (LSOA) level datasets to analyse the patterns of emissions. Some of these are in the repo as they are public access, others are not (or too large).
All analysis is at LSOA level. Cautions on inference from area level data apply.
See https://www.creds.ac.uk/why-we-built-a-place-based-carbon-calculator/
“The highest carbon areas have an average per person footprint more than eight times larger than the lowest carbon areas.”
“We are not effectively targeting decarbonisation policies in high carbon areas. For example, the recently collapsed Green Homes Grants scheme provided a grant to cover 66% of the cost (up to £5,000) of retrofitting homes. For people claiming certain benefits, the cap was raised to 100% and £10,000. But the calculator shows that the big polluters are the large homes in very wealthy areas. In these neighbourhoods, the issue is not affordability but motivation. For high income households, energy costs are a small proportion of their expenditure and so the cost savings for retrofitting their home are inconsequential. As there are no policy “sticks” to incentivise action in the collective interest it is unsurprising that high carbon neighbourhoods have not prioritised decarbonisation."
Source: https://www.carbon.place/
Notes:
region | nLSOAs | mean_KgCo2ePerCap | sd_KgCo2ePerCap |
805 | 9,834.6 | 2,737.8 | |
East | 3,392 | 8,886.3 | 3,134.0 |
East Midlands | 2,713 | 7,835.8 | 3,195.0 |
London | 4,826 | 9,116.2 | 3,137.5 |
North East | 1,634 | 6,801.0 | 4,123.3 |
North West | 4,463 | 7,430.7 | 2,862.8 |
South East | 5,278 | 9,813.2 | 3,648.3 |
South West | 3,059 | 7,930.2 | 2,780.2 |
West Midlands | 3,403 | 7,506.7 | 4,037.7 |
Yorkshire and The Humber | 3,271 | 7,419.2 | 2,772.2 |
Now we need to convert the per capita to totals and then use the number of electricity meters as a proxy for the number of dwellings
Ideally we’d have Census 2021 data but we don’t have it yet. So instead we’ll use the number of electricity meters for 2018 which aligns with the CREDS data (might be an over-estimate if a dwelling has 2…)
First check the n electricity meters logic…
## LSOA11NM WD18NM nGasMeters nElecMeters epc_total
## 1: Aylesbury Vale 012A Riverside 3373 3175 3110
## 2: Test Valley 003B St Mary's 2641 2487 2230
## 3: Milton Keynes 017H Broughton 2517 2382 2460
## 4: Test Valley 003A Alamein 2513 2638 2350
## 5: Peterborough 019D Stanground South 2261 2178 1880
## 6: Swindon 008B Blunsdon and Highworth 2227 2166 2020
## LSOA11NM WD18NM nGasMeters nElecMeters epc_total
## 1: Newham 013G Stratford and New Town 731 6351 6350
## 2: Wandsworth 002B Queenstown 675 3282 1700
## 3: Aylesbury Vale 012A Riverside 3373 3175 3110
## 4: Newham 037E Royal Docks 574 3116 2900
## 5: Lewisham 012E Lewisham Central 568 2893 2730
## 6: Test Valley 003A Alamein 2513 2638 2350
LSOA11NM | WD18NM | nGasMeters | nElecMeters | epc_total |
Aylesbury Vale 012A | Riverside | 3,373 | 3,175 | 3,110 |
Test Valley 003B | St Mary's | 2,641 | 2,487 | 2,230 |
Milton Keynes 017H | Broughton | 2,517 | 2,382 | 2,460 |
Test Valley 003A | Alamein | 2,513 | 2,638 | 2,350 |
Peterborough 019D | Stanground South | 2,261 | 2,178 | 1,880 |
Swindon 008B | Blunsdon and Highworth | 2,227 | 2,166 | 2,020 |
Check that the number of electricity meters reasonably correlates with the number of EPCs from the CREDS data. We would not expect the number of gas meters to correlate due to non-gas dwellings etc.
There may also be difficulties where there are multiple meters per property - e.g. one ‘standard’ and one ‘economy 7.’ Really should switch to using address counts from postcode file.
Check that the assumption seems sensible…
Check for outliers - what might this indicate?
We want to present the analysis in ‘per dwelling’ or ‘per household’ terms so we need to convert the total kg CO2e values to per dwelling values by dividing by the number of electricity meters.
## # Summary of per dwelling values
| Name | …[] |
| Number of rows | 32039 |
| Number of columns | 9 |
| Key | NULL |
| _______________________ | |
| Column type frequency: | |
| numeric | 9 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| CREDStotal_kgco2e_pdw | 0 | 1 | 19490.24 | 9188.71 | 3587.62 | 12947.16 | 18275.82 | 24069.71 | 586372.22 | ▇▁▁▁▁ |
| CREDSgas_kgco2e2018_pdw | 0 | 1 | 2465.42 | 851.99 | 3.92 | 2037.62 | 2434.68 | 2868.68 | 71095.56 | ▇▁▁▁▁ |
| CREDSelec_kgco2e2018_pdw | 0 | 1 | 1021.63 | 220.17 | 40.55 | 888.82 | 977.15 | 1092.44 | 4046.23 | ▂▇▁▁▁ |
| CREDSmeasuredHomeEnergy_kgco2e2018_pdw | 0 | 1 | 3487.05 | 912.74 | 458.61 | 2978.41 | 3398.85 | 3894.92 | 72698.53 | ▇▁▁▁▁ |
| CREDSotherEnergy_kgco2e2011_pdw | 0 | 1 | 175.08 | 336.37 | 0.00 | 40.20 | 69.74 | 136.09 | 6877.09 | ▇▁▁▁▁ |
| CREDSallHomeEnergy_kgco2e2018_pdw | 0 | 1 | 3662.13 | 910.23 | 912.57 | 3125.71 | 3558.65 | 4082.50 | 76436.03 | ▇▁▁▁▁ |
| CREDScar_kgco2e2018_pdw | 0 | 1 | 2200.93 | 1038.37 | 127.70 | 1529.01 | 2142.16 | 2797.44 | 89700.00 | ▇▁▁▁▁ |
| CREDSvan_kgco2e2018_pdw | 1 | 1 | 366.16 | 2774.12 | 0.05 | 137.01 | 217.71 | 342.60 | 344822.80 | ▇▁▁▁▁ |
| CREDSpersonalTransport_kgco2e2018_pdw | 1 | 1 | 2567.13 | 2987.05 | 141.80 | 1742.05 | 2422.45 | 3151.56 | 346819.80 | ▇▁▁▁▁ |
Examine patterns of per dwelling emissions for sense.
Figure 5.1 shows the LSOA level per dwelling ‘all emissions’ in Tonnes CO2e as estimated by the CREDS tool against the Index of Multiple Deprivation (IMD) score and uses the size of the points to represent the % of dwellings with electric heating. Colour is used to represent the IMD decile where decile 1 is the 10% least deprived.
## Per dwelling T CO2e - all emissions
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 5.1: Scatter of LSOA level all per dwelling emissions against IMD score
## Correlation test (pwcorr)
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDStotal_kgco2e_pdw
## t = -123.51, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.5753081 -0.5604715
## sample estimates:
## cor
## -0.5679359
## Total emissions per dwelling (LSOA level) summary
## LSOA11CD WD18NM IMD_Decile_label All_Tco2e_per_dw
## Length:32039 Length:32039 1 (10% most deprived) : 3282 Min. : 3.588
## Class :character Class :character 10 (10% least deprived): 3280 1st Qu.: 12.947
## Mode :character Mode :character 2 : 3271 Median : 18.276
## 9 : 3247 Mean : 19.490
## 3 : 3237 3rd Qu.: 24.070
## 8 : 3220 Max. :586.372
## (Other) :12502
LSOA11CD | WD18NM | IMD_Decile_label | All_Tco2e_per_dw |
E01031998 | Durrington and Larkhill | 9 | 586.4 |
E01009320 | Sheldon | 4 | 364.7 |
E01033484 | Park East | 1 (10% most deprived) | 203.7 |
E01010151 | Knowle | 8 | 171.2 |
E01019556 | Holmebrook | 2 | 160.2 |
E01033749 | Greenbank | 6 | 139.7 |
LSOA11CD | WD18NM | IMD_Decile_label | All_Tco2e_per_dw |
E01033583 | Stratford and New Town | 3 | 3.6 |
E01033726 | Eltham West | 2 | 3.8 |
E01015895 | Victoria | 1 (10% most deprived) | 4.3 |
E01008703 | Hendon | 1 (10% most deprived) | 4.4 |
E01005133 | Ancoats & Beswick | 1 (10% most deprived) | 4.9 |
E01004562 | Queenstown | 3 | 5.0 |
Figure 5.2 uses the same plotting method to show emissions per dwelling due to gas use.
## Per dwelling T CO2e - gas emissions
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.92 2037.62 2434.68 2465.42 2868.68 71095.56
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 5.2: Scatter of LSOA level gas per dwelling emissions against IMD score
## Correlation test (pwcorr)
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSgas_kgco2e2018_pdw
## t = -70.089, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3740796 -0.3550910
## sample estimates:
## cor
## -0.3646232
Figure 5.3 uses the same plotting method to show emissions per dwelling due to electricity use.
## Per dwelling T CO2e - elec emissions
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 5.3: Scatter of LSOA level elec per dwelling emissions against IMD score - who emits?
## Correlation test (pwcorr)
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSelec_kgco2e2018_pdw
## t = -77.608, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4069829 -0.3885483
## sample estimates:
## cor
## -0.3978058
Figure 5.4 uses the same plotting method to show emissions per dwelling due to other energy use. This should be higher for off-gas areas which tend to be rural areas so we also present analysis by the LSOA’s urban/rural classification.
## Per dwelling T CO2e - elec emissions
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 5.4: Scatter of LSOA level other energy per dwelling emissions against IMD score - who emits?
## Correlation test (pwcorr)
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSelec_kgco2e2018_pdw
## t = -77.608, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4069829 -0.3885483
## sample estimates:
## cor
## -0.3978058
RUC11 | mean_gas_kgco2e | mean_elec_kgco2e | mean_other_energy_kgco2e |
Rural town and fringe | 2,536.8 | 1,083.0 | 274.2 |
Rural town and fringe in a sparse setting | 2,254.1 | 993.7 | 271.6 |
Rural village and dispersed | 1,879.3 | 1,481.9 | 1,131.9 |
Rural village and dispersed in a sparse setting | 1,015.1 | 1,405.2 | 1,440.1 |
Urban city and town | 2,456.0 | 991.9 | 86.3 |
Urban city and town in a sparse setting | 2,230.2 | 945.0 | 124.6 |
Urban major conurbation | 2,552.2 | 981.3 | 108.7 |
Urban minor conurbation | 2,582.8 | 913.9 | 124.0 |
Check whether all measured energy emissions combined (gas & electricity) correlate with all emissions (in this data).
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$CREDStotal_kgco2e_pdw and selectedLsoasDT$CREDSmeasuredHomeEnergy_kgco2e2018_pdw
## t = 158.14, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.6559143 0.6682142
## sample estimates:
## cor
## 0.6621088
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Do we see strong correlations? If so in theory we could (currently) use measured energy emissions as a proxy for total emissions.
Repeat for all home energy - includes estimates of emissions from oil etc
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$CREDStotal_kgco2e_pdw and selectedLsoasDT$CREDSallHomeEnergy_kgco2e2018_pdw
## t = 177.83, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.6992585 0.7102801
## sample estimates:
## cor
## 0.7048118
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
How does the correlation look now?
We don’t expect to use this data as it is already taxed in a way that relates to emissions (?)
Figure 5.5 uses the same plotting method to show emissions per dwelling due to van use. Again, we present analysis by the LSOA’s urban/rural classification.
## Per dwelling T CO2e - car emissions
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 5.5: Scatter of LSOA level car use per dwelling emissions against IMD score
## Correlation with IMD score (pwcorr)
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDScar_kgco2e2018_pdw
## t = -119.05, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.5613723 -0.5461891
## sample estimates:
## cor
## -0.5538267
RUC11 | mean_car_kgco2e | mean_van_kgco2e |
Rural town and fringe | 2,882.6 | 412.8 |
Rural town and fringe in a sparse setting | 2,198.1 | 347.0 |
Rural village and dispersed | 3,754.9 | 664.4 |
Rural village and dispersed in a sparse setting | 3,095.9 | 586.6 |
Urban city and town | 2,280.4 | 379.3 |
Urban city and town in a sparse setting | 1,761.6 | 300.2 |
Urban major conurbation | 1,719.0 | |
Urban minor conurbation | 1,899.4 | 307.6 |
Figure 5.6 uses the same plotting method to show emissions per dwelling due to van use.
## Per dwelling T CO2e - van emissions
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 5.6: Scatter of LSOA level van use per dwelling emissions against IMD score
## Correlation with IMD score (pwcorr)
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSvan_kgco2e2018_pdw
## t = -0.79155, df = 32036, p-value = 0.4286
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.015371712 0.006528074
## sample estimates:
## cor
## -0.004422349
In order to estimate the LSOA level retrofit costs, we need to impute the EPC counts in each LSOA. We do this using the number of electricity meters as the presumed number of dwellings and the observed % of EPCs in each band for all dwellings with EPCs which is provided by the CREDS data. This assumes that if we had EPCs for all dwellings then the % in each band in each LSOA would stay the same. This is quite a bold assumption…
Note that the EPC database is continuously updated so more recent upgrades will not be captured in the data used for this analysis. This means the total retrofit costs are likely to be an over-estimate. The extent of this over-estimate would require the use of an updated (current) EPC data extract and is left for future work.
## N EPCs
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 30.0 315.0 390.0 434.2 503.0 6350.0
## N elec meters
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 36.0 623.0 692.0 736.3 809.0 6351.0
Correlation between high % EPC F/G or A/B and deprivation?
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Now we need to convert the % to dwellings using the number of electricity meters (see above).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Case studies:
BEIS/ETC Carbon ‘price’
EU carbon ‘price’
Scenario 1: apply the central value Scenario 2: apply the low/central/high as a rising block tariff for each emissions source. Set threhsolds to 33% and 66% (in absence of any other guidance!)
Table 5.6 below shows the overall £ GBP total for the case study area in £M under Scenario 1.
nLSOAs | beis_GBPtotal_c | beis_total_c_gas | beis_GBPtotal_c_elec |
32,039 | 107,100.4 | 13,847.3 | 5,871.4 |
region | nLSOAs | beis_GBPtotal_c | beis_total_c_gas | beis_GBPtotal_c_elec |
South East | 5,278 | 20,772.1 | 2,272.1 | 1,038.8 |
London | 4,826 | 19,038.1 | 2,048.9 | 870.5 |
North West | 4,463 | 12,703.6 | 1,952.4 | 766.1 |
East | 3,392 | 12,199.3 | 1,450.0 | 668.6 |
West Midlands | 3,403 | 10,191.7 | 1,475.8 | 604.8 |
South West | 3,059 | 9,784.3 | 1,147.2 | 613.9 |
Yorkshire and The Humber | 3,271 | 9,495.3 | 1,494.1 | 550.2 |
East Midlands | 2,713 | 8,687.1 | 1,249.5 | 503.9 |
North East | 1,634 | 4,228.9 | 757.3 | 254.7 |
Figure 5.7: Proportion of total emissions due to gas & electricity use by region covered
The table below shows the mean per dwelling value rounded to the nearest £10.
All_emissions | Gas | Electricity | Gas + Electricity |
4,775.1 | 604.0 | 250.3 | 854.3 |
Figure ?? shows the total £k per LSOA and £ per dwelling revenue using BEIS central carbon price plotted against IMD score. The tables show the LSOAs with the highest and lowest values.
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 5.8: £k per LSOA revenue using BEIS central carbon price
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 5.9: £k per LSOA revenue using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 879 3172 4478 4775 5897 143661
LSOA11CD | LSOA01NM | WD18NM | T_CO2e_pdw | GBP_allEmissions_levy |
E01031998 | Wiltshire 045C | Durrington and Larkhill | 586.4 | 143,661.2 |
E01009320 | Birmingham 081F | Sheldon | 364.7 | 89,343.8 |
E01033484 | Darlington 008F | Park East | 203.7 | 49,897.4 |
E01010151 | Solihull 026A | Knowle | 171.2 | 41,947.7 |
E01019556 | Chesterfield 010C | Holmebrook | 160.2 | 39,241.7 |
E01033749 | Liverpool 042F | Greenbank | 139.7 | 34,224.3 |
LSOA11CD | LSOA01NM | WD18NM | T_CO2e_pdw | GBP_allEmissions_levy |
E01033583 | Newham 013G | Stratford and New Town | 3.6 | 879.0 |
E01033726 | Greenwich 034E | Eltham West | 3.8 | 933.1 |
E01015895 | Southend-on-Sea 010A | Victoria | 4.3 | 1,050.9 |
E01008703 | Sunderland 013B | Hendon | 4.4 | 1,070.5 |
E01005133 | Manchester 013D | Ancoats & Beswick | 4.9 | 1,202.1 |
E01004562 | Wandsworth 002B | Queenstown | 5.0 | 1,216.5 |
Figure ?? repeats the analysis but just for gas.
Anything unusual?
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 5.10: £k per LSOA incurred via gas using BEIS central carbon price
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 5.11: £k per LSOA incurred via gas using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.96 499.22 596.50 604.03 702.83 17418.41
LSOA11CD | LSOA01NM | WD18NM | gas_T_CO2e_pdw | GBP_gas_levy_perdw |
E01031998 | Wiltshire 045C | Durrington and Larkhill | 71.1 | 17,418.4 |
E01000213 | Barnet 033F | Garden Suburb | 7.4 | 1,802.1 |
E01023812 | Three Rivers 004A | Chorleywood North & Sarratt | 7.2 | 1,756.1 |
E01023841 | Three Rivers 011C | Moor Park & Eastbury | 6.9 | 1,696.8 |
E01004114 | Sutton 025D | Cheam | 6.7 | 1,646.7 |
E01023813 | Three Rivers 004B | Chorleywood North & Sarratt | 6.7 | 1,646.1 |
LSOA11CD | LSOA01NM | WD18NM | gas_T_CO2e_pdw | GBP_gas_levy_perdw |
E01026645 | King's Lynn and West Norfolk 002A | Brancaster | 0.0 | 3.9 |
E01026718 | King's Lynn and West Norfolk 004D | Valley Hill | 0.0 | 3.5 |
E01027382 | Northumberland 002D | Norham and Islandshires | 0.0 | 3.3 |
E01020864 | County Durham 064G | Evenwood | 0.0 | 3.2 |
E01032746 | Southampton 029F | Bargate | 0.0 | 3.1 |
E01020534 | West Dorset 003F | Maiden Newton | 0.0 | 1.0 |
Figure ?? repeats the analysis for electricity.
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 5.12: £k per LSOA incurred via electricity using BEIS central carbon price
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 5.13: £k per LSOA incurred via electricity using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 9.934 217.760 239.403 250.299 267.648 991.327
LSOA11CD | LSOA01NM | WD18NM | elec_T_CO2e_pdw | GBP_elec_levy_perdw |
E01000206 | Barnet 033B | Garden Suburb | 4.0 | 991.3 |
E01030692 | Runnymede 005D | Virginia Water | 3.4 | 823.2 |
E01030342 | Elmbridge 018B | Oxshott and Stoke D'Abernon | 3.3 | 819.8 |
E01030346 | Elmbridge 016A | Weybridge St George's Hill | 3.3 | 803.8 |
E01004690 | Westminster 019D | Knightsbridge and Belgravia | 2.9 | 704.6 |
E01003465 | Merton 002D | Village | 2.9 | 703.9 |
LSOA11CD | LSOA01NM | WD18NM | elec_T_CO2e_pdw | GBP_elec_levy_perdw |
E01008777 | Sunderland 026C | St Chad's | 0.5 | 124.5 |
E01024604 | Swale 014C | St Ann's | 0.5 | 118.3 |
E01002862 | Kensington and Chelsea 014E | Stanley | 0.5 | 111.4 |
E01033736 | Greenwich 004H | Woolwich Riverside | 0.4 | 106.3 |
E01004739 | Westminster 024E | Tachbrook | 0.3 | 81.4 |
E01010257 | Walsall 007E | Aldridge North and Walsall Wood | 0.0 | 9.9 |
Figure ?? shows the same analysis for measured energy (elec + gas)
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 5.14: £k per LSOA incurred via electricity and gas using BEIS central carbon price
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 5.15: £k per LSOA incurred via electricity and gas using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 112.4 729.7 832.7 854.3 954.3 17811.1
Applied to per dwelling values (not LSOA total) - may be methodologically dubious?
Cut at 25%, 50% - so any emissions over 50% get high carbon cost
## Cuts for total per dw
## 0% 25% 50% 75% 100%
## 3587.624 12947.165 18275.816 24069.709 586372.222
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
| Name | …[] |
| Number of rows | 32039 |
| Number of columns | 3 |
| Key | NULL |
| _______________________ | |
| Column type frequency: | |
| numeric | 3 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| V1 | 0 | 1 | 19.49 | 9.19 | 3.59 | 12.95 | 18.28 | 24.07 | 586.37 | ▇▁▁▁▁ |
| beis_GBPtotal_sc2_perdw | 0 | 1 | 3727.91 | 3031.87 | 437.69 | 1579.54 | 2885.00 | 5011.43 | 211376.45 | ▇▁▁▁▁ |
| beis_GBPtotal_sc2 | 0 | 1 | 2528423.89 | 1653321.47 | 543095.20 | 1222262.12 | 2220433.98 | 3356216.64 | 84377314.16 | ▇▁▁▁▁ |
nLSOAs | sum_total_sc1 | sum_total_sc2 |
32,039 | 107,100.4 | 81,008.2 |
Figure 5.16 compares the £ levy under each scenario for all consumption.
Figure 5.16: Comparing £ levy under each scenario
## [1] 9086.681
## [1] 3997.045
nLSOAs | sumAllConsEmissions_GBP | sumGasEmissions_GBP | sumElecEmissions_GBP |
32,039 | 81,008.2 | 9,086.7 | 3,997.0 |
region | nLSOAs | sumAllConsEmissions_GBP | sumGasEmissions_GBP | sumElecEmissions_GBP | sumPop |
East | 3,392 | 9,526.3 | 939.1 | 487.4 | 5,818,700 |
East Midlands | 2,713 | 6,247.7 | 827.1 | 339.5 | 4,693,551 |
London | 4,826 | 15,649.7 | 1,389.4 | 583.8 | 8,889,572 |
North East | 1,634 | 2,714.3 | 502.9 | 143.4 | 2,622,240 |
North West | 4,463 | 8,777.3 | 1,282.6 | 491.5 | 7,236,660 |
South East | 5,278 | 17,507.7 | 1,503.9 | 766.7 | 8,973,952 |
South West | 3,059 | 6,810.0 | 654.0 | 431.1 | 5,213,266 |
West Midlands | 3,403 | 7,270.4 | 976.6 | 410.8 | 5,765,703 |
Yorkshire and The Humber | 3,271 | 6,504.8 | 1,011.1 | 342.9 | 5,405,939 |
## region nLSOAs sumAllConsEmissions_GBP sumGasEmissions_GBP
## 1: South East 5278 17507.695 1503.8874
## 2: London 4826 15649.748 1389.3724
## 3: East 3392 9526.267 939.0531
## 4: North West 4463 8777.331 1282.6361
## 5: West Midlands 3403 7270.355 976.6076
## 6: South West 3059 6809.964 653.9993
## 7: Yorkshire and The Humber 3271 6504.827 1011.0844
## 8: East Midlands 2713 6247.711 827.1231
## 9: North East 1634 2714.274 502.9172
## sumElecEmissions_GBP sumPop
## 1: 766.6684 8973952
## 2: 583.7890 8889572
## 3: 487.3759 5818700
## 4: 491.5296 7236660
## 5: 410.8372 5765703
## 6: 431.0802 5213266
## 7: 342.8749 5405939
## 8: 339.4664 4693551
## 9: 143.4232 2622240
Figure 5.17: Contribution to sum levy £ GBP by source
Source: English Housing Survey 2018 Energy Report
Model excludes EPC A, B & C (assumes no need to upgrade)
Adding these back in would increase the cost… obvs
Table 5.13 reports total retofit costs.
## To retrofit D-E (£m)
## [1] 177847.9
## Number of dwellings: 13372024
## To retrofit F-G (£m)
## [1] 26752.52
## Number of dwellings: 998229
## To retrofit D-G (£m)
## [1] 204600.4
## To retrofit D-G (mean per dwelling)
## [1] 14163.45
meanPerLSOA_GBPm | total_GBPm |
6.4 | 204,600.4 |
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 5.18 shows the LSOA level retofit costs per dwelling by IMD decile.
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 5.18: LSOA level retofit costs per dwelling by IMD score
Totals
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Repeat per dwelling
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Totals
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Repeat per dwelling
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 5.19 shows years to pay under Scenario 1 (all emissions)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.09599 2.40262 3.17210 3.53055 4.44332 15.64765 1
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 5.19: Years to pay under Scenario 1 (all em issions)
## Median years: NA
Figure 5.20 shows years to pay under Scenario 1 (energy emissions)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.7742 14.7584 16.8635 17.5709 19.2684 118.6634 1
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 5.20: Years to pay under Scenario 1 (energy emissions)
## Median years: NA
Figure 5.21 shows the year 1 outcome if levy is shared equally (all emissions levy).
Figure 5.21: Year 1 outcome if levy is shared equally (all emissions levy)
LSOA11CD | LSOA11NM | WD18NM | retrofitSum | yearsToPay | epc_D_pc | epc_E_pc | epc_F_pc | epc_G_pc |
E01019012 | Cornwall 054E | St Ives East | 26,389,383.3 | 32.3 | 0.3 | 0.2 | 0.1 | 0.1 |
E01018781 | Cornwall 034B | Rame Peninsular | 22,060,171.6 | 49.7 | 0.3 | 0.3 | 0.2 | 0.1 |
E01027840 | Scarborough 002C | Mulgrave | 21,959,636.2 | 32.7 | 0.3 | 0.2 | 0.2 | 0.1 |
E01021988 | Tendring 018A | Golf Green | 21,701,516.6 | 36.6 | 0.3 | 0.3 | 0.2 | 0.1 |
E01018766 | Cornwall 028D | Looe West, Lansallos and Lanteglos | 21,409,249.4 | 46.6 | 0.2 | 0.3 | 0.3 | 0.1 |
E01020541 | West Dorset 002C | Sherborne East | 21,066,562.5 | 31.5 | 0.3 | 0.3 | 0.2 | 0.1 |
E01026741 | North Norfolk 004A | High Heath | 20,793,004.0 | 35.2 | 0.3 | 0.3 | 0.2 | 0.1 |
E01019002 | Cornwall 070B | Newlyn and Mousehole | 20,415,414.0 | 45.0 | 0.2 | 0.3 | 0.3 | 0.2 |
E01018982 | Cornwall 057C | Hayle North | 20,411,151.0 | 40.5 | 0.3 | 0.2 | 0.2 | 0.1 |
E01027374 | Northumberland 003A | Bamburgh | 19,563,518.6 | 30.0 | 0.3 | 0.2 | 0.1 | 0.1 |
Figure 5.22 shows the year 1 outcome if levy is shared equally (energy emissions levy).
Figure 5.22: Year 1 outcome if levy is shared equally (energy emissions levy)
What happens in Year 2 totally depends on the rate of upgrades… given the supply chain & capacity issues it’s likely that the levy would build up a substantial ‘headroom’ that could then be spent over time…
Figure 5.23 shows years to pay under Scenario 2 (all emissions)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.06524 2.83119 4.88954 5.92627 8.83376 31.42356 1
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 5.23: Years to pay under Scenario 2 (all em issions)
## Median years: NA
Figure 5.24 shows years to pay under Scenario 2 (energy emissions)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.7742 14.7584 16.8635 17.5709 19.2684 118.6634 1
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 5.24: Years to pay under Scenario 2 (energy emissions)
Figure 5.25 shows the year 1 outcome if levy is shared equally (all emissions levy).
Figure 5.25: Year 1 outcome if levy is shared equally (all emissions levy)
Figure 5.26 shows the year 1 outcome if levy is shared equally (energy emissions levy).
Figure 5.26: Year 1 outcome if levy is shared equally (energy emissions levy)
What happens in Year 2 totally depends on the rate of upgrades…
Figure 5.27 compares pay-back times for the two scenarios - who does the rising block tariff help?
Figure 5.27: Comparing pay-back times across scenarios
x = y line shown for clarity
I don’t know if this will work…
## Doesn't